Note
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9.2.8. Controlling the contrast of the background when plotting¶
The dim argument controls the contrast of the background.
dim modifies the contrast of this image: dim=0 leaves the image unchanged, negative values of dim enhance it, and positive values decrease it (dim the background).
This dim argument may also be useful for the plot_roi function used to display ROIs on top of a background image.
9.2.8.1. Retrieve the data: the localizer dataset with contrast maps¶
from nilearn import datasets
localizer_dataset = datasets.fetch_localizer_button_task()
# Contrast map of motor task
localizer_tmap_filename = localizer_dataset.tmap
# Subject specific anatomical image
localizer_anat_filename = localizer_dataset.anat
Out:
/home/nicolas/anaconda3/envs/nilearn/lib/python3.8/site-packages/numpy/lib/npyio.py:2405: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.
output = genfromtxt(fname, **kwargs)
9.2.8.2. Plotting with enhancement of background image with dim=-.5¶
from nilearn import plotting
plotting.plot_stat_map(localizer_tmap_filename,
bg_img=localizer_anat_filename,
cut_coords=(36, -27, 66),
threshold=3, title="dim=-.5",
dim=-.5)

Out:
<nilearn.plotting.displays.OrthoSlicer object at 0x7ff045edb0a0>
9.2.8.3. Plotting with no change of contrast in background image with dim=0¶
plotting.plot_stat_map(localizer_tmap_filename,
bg_img=localizer_anat_filename,
cut_coords=(36, -27, 66),
threshold=3, title="dim=0",
dim=0)

Out:
<nilearn.plotting.displays.OrthoSlicer object at 0x7ff04479f7f0>
9.2.8.4. Plotting with decrease of contrast in background image with dim=.5¶
plotting.plot_stat_map(localizer_tmap_filename,
bg_img=localizer_anat_filename,
cut_coords=(36, -27, 66),
threshold=3, title="dim=.5",
dim=.5)

Out:
<nilearn.plotting.displays.OrthoSlicer object at 0x7ff0447ac070>
9.2.8.5. Plotting with more decrease in contrast with dim=1¶
plotting.plot_stat_map(localizer_tmap_filename,
bg_img=localizer_anat_filename,
cut_coords=(36, -27, 66),
threshold=3, title="dim=1",
dim=1)
plotting.show()

Total running time of the script: ( 0 minutes 4.769 seconds)